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Decision-based Learning

Decision-Based Learning (DBL) is a pedagogical method that organizes instruction around the decision-making process of an expert (such as an instructor). Using a form of cognitive task analysis (CTA), experts analyze their thought process for the range of problem types in the domain. Decision points are identified and represented in an expert decision model (EDM; Swan et al., 2020).
Keywords: Conditional Knowledge, Functional Expertise, Instructional Design, Pedagogical Method, Schema Building

An EDM may be linear, branching, or looping or may show a combination of these patterns. It should also focus on a single learning outcome and the object of analysis for that learning outcome. The learning outcome describes the culminating action learners take at the end of a decision path within an EDM. Plummer et al. (2022) describe this culminating action as follows:

At the end of each decision path within the decision model (EDM) is a culminating action or decision. For example, in [one] chemistry course, the culminating action at the end of their decision model was to determine if the correct technique had been located to solve a heat and enthalpy problem (Sansom et al., 2019). In a qualitative inquiry course, the culminating action was to determine the credibility of a published qualitative study (Owens & Mills, 2021). Finally, in a mechanical engineering course, the culminating action was to determine the design and performance of a machine element (Nelson, 2021). (p. 5)

This process identifies the conditions at each decision point that invoke relevant concepts and procedures (see Figure 1 for an example). Such conditional knowledge is often taken for granted by experts (cf., "expert blind spots") and, thus, remains invisible to students in most forms of instruction. However, conditional knowledge is essential for successfully analyzing situations and selecting an appropriate course of action. Conditional knowledge is also a necessary foundation for well-developed conceptual understanding.

Figure 1

An Excerpt from an Expert Decision Model Used in a Basic Statistics Course

decision model of basic statistics

Within an EDM, multiple problems are created for each problem type. Problems are cosmetically different but contain the defining conditions for the given problem type. Initially, learners may have difficulty distinguishing cosmetic conditions from defining conditions. With practice, learners develop the ability to distinguish defining conditions that lead to resolution of the problem. Thus, a robust bank of problems for each problem type is ideal in order to provide students with ample opportunity to practice.

Different conditions become salient at different decision points in the process. Consequently, instruction is tailored and available at each decision point. The goal is to provide just enough instruction just-in-time to make the current decision. The concise nature of this instruction helps students focus on and separate the defining condition for that decision from other sibling or cosmetic conditions in the scenario. In this way, students begin to develop a functional schema of the domain.

In this process, instruction is initially highly-scaffolded. Students are presented with a realistic scenario and are guided through each relevant decision point of a decision model until they come to an endpoint where they take a final action (e.g., select, analyze, solve, and evaluate the example). At each decision, just enough just-in-time instruction helps them determine the best option for the scenario at that decision point. As they repeatedly take many concrete scenarios through the decision model, they begin to recognize patterns and to schematize their knowledge. However, students can also over-rely on the model. Frequent, low-stakes assessments that require equal performance without the model are essential to prompt students to internalize their learning. Once the decision model is internalized, students may be given ill-defined problems requiring them to adapt and even restructure their newly gained knowledge without the model as scaffolding.

Throughout this process, students are expected to conditionalize their knowledge by developing functional patterns that provide a framework for conceptual understanding and application.

References

Nelson, T. G. (2021). Exploring decision-based learning in an engineering context. In N. Wentworth, K. J. Plummer, & R. H. Swan (Eds.), Decision-based learning: An innovative pedagogy that unpacks expert knowledge for the novice learner (pp. 55–66). Emerald Publishing Limited. 

Owens, M. A., & Mills, E. R. (2021). Using decision-based learning to teach qualitative research evaluation. In N. N. Wentworth, K. J. Plummer, & R. H. Swan (Eds.), Decision-based learning: An innovative pedagogy that unpacks expert knowledge for the novice learner (pp. 93–102). Emerald Publishing Limited. 

Plummer, K. J., Kebritchi, M., Leary, H. M. & Halverson, D. (2022). Enhancing Critical Thinking Skills through Decision-Based Learning. Innovative Higher Education, 1-25. https://doi.org/10.1007/s10755-022-09595-9 

Sansom, R. L., Suh, E., & Plummer, K. J. (2019). Decision-based learning:′′If I just knew which equation to use, I know I could solve this problem!′′ Journal of Chemical Education, 96(3), 445–454.

Swan, R. H., Plummer, K. J., & West, R. E. (2020). Toward functional expertise through formal education: Identifying an opportunity for higher education. Educational Technology Research and Development, 68(5), 2551-2568.

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